Cluster Analysis on IRIS Data Using Partitional Clustering Algorithms

Implementation of partitional clustering algorithms for cluster analysis on the IRIS dataset, which contains measurements from three distinct species of iris flowers. The dataset comprises 3 pattern classes with 4 feature dimensions, containing 50 pattern samples per class for a total of 150 samples. Key clustering algorithms like K-Means or hierarchical methods can be applied to identify natural groupings and evaluate clustering performance metrics.